Course Outline
The objective of the course is to present linear and non-linear
models for both conditional mean and conditional variances. The Extreme
Values Theory, Theory of Copulas and Additional Topics (Models for High
Frequency Data, Continuous Time Modelsor High Dimensional Model) will
also be presented.
Stylized Facts in Financial Data
Primeiro, iremos buscar as bases de dados, sendo
c("^IXIC","AAPL") o Ăndice NASDAQ e a aĂ§Ă£o da Apple,
respectivamente:
options(scipen=999)
library(tidyquant)
library(tidyverse)
library(plotly)
df <- tq_get(c("^BVSP","BBAS3.SA"), get = "stock.prices", from = "2016-01-01", complete_cases = FALSE)
Faremos entĂ£o o Retorno Simples e Retorno
Composto e para o fechamento do dia (Close):
ts <- df |>
group_by(symbol) |>
summarise(date = date,
close = close ,
lClose = log(close),
rsClose = ((close-Lag(close))/Lag(close))*100,
rcClose = (lClose - Lag(lClose))*100
)
pl1 <- ts |> group_by(symbol) |>
ggplot(aes(x = date, y = rsClose, color = symbol)) +
geom_line(size = 0.5) +
scale_y_continuous() +
labs(title = "Retorno Simples",
x = "", y = "Retorno", color = "") +
facet_wrap(~ symbol, nrow = 2, scales = "free_y") +
theme_tq()
ggplotly(pl1)
pl2 <- ts |> group_by(symbol) |>
ggplot(aes(x = date, y = rcClose, color = symbol)) +
geom_line(size = 0.5) +
scale_y_continuous() +
labs(title = "Retorno Composto",
x = "", y = "Retorno", color = "") +
facet_wrap(~ symbol, nrow = 2, scales = "free_y") +
theme_tq()
ggplotly(pl2)
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